1. Opportunities for Big Data in Medical Records Mike Conlon, PhD mconlon@ufl.edu www.ctsi.ufl.edu

2. A Medical Record is … A legal document. MD accountable for contents. Data belongs to the patient Protected Health Information. Security, Privacy Records of visits Patient identification and dates – who was seen when Notes – transcribed text from MD dictation Lab reports Pointers to images Billing – diagnostic codes, procedure codes, prescriptions Organized for care – rapid access to historical information for individual patients

3. Problems with Medical Records Incomplete. Focused on provider, not patient. One patient may have many providers MD bias. Data elements reported as needed for care Notes -> text, not structured data. Few common data elements Immature, nonstandard use of vocabulary for common elements. Procedures (CPT), diagnoses (ICD9/10), medications (RxNorm), UMLS (100 vocabularies, 1 million concepts, 5 million concept names)

4. What’s Happening – New Data Patient reported outcomes. Quality of Life, other self-reported measures Genetics – 3 billion base pairs. $7K, under $1K in 2 years. 1 Gig per person. Metabolomics – small molecule distributions at the cellular level. Time, disease, location, function varying. PubChem 19 million small organic molecules Microbiome – gene sequencing of symbiotic organisms (10 times the number of human cells (100 trillion)). Time varying.

5. What’s Happening – Pooling Data Collecting data across providers Creating a patient-centric record. Personal Health records. Microsoft HealthVault. Google Health discontinued. Some large collections VA – 8M patients per year. Common medical record Insurance/HMO claims data – United Health Care (70M), WellPoint (68M), Kaiser Permanente (9M) Harvard Hospitals (20). SHRINE system UF and partners. Potential for 5M patients

6. What’s Happening – UF Epic electronic medical record for UF Health Integrated Data Repository to provide data for research organized for query and analysis. Since June 2011: 350K pts, 2M visits, 11M procedures, 14M diagnoses Under a consent process, addition of scoped genetic data (1,500 pts/yr; 256 SNPs) Under IRB approval, pooling de-identified data across systems in Florida (Gainesville, Jacksonville, Orlando)

7. Improving Care With Big Data Show me patients like my current patient (disease, condition, history, genome, metabolome, microbiome) Show me treatment alternatives those patients received n 1 patients received t 1 n 2 patients received t 2 n 3 patients received t 3 Show outcomes from these alternatives Patients receiving t 1 had these outcomes …. Patients receiving t 2 had these outcomes …. Patients receiving t 3 had these outcomes …. Generate recommendation based on patient’s concerns for risk/reward

8. Reducing Time to New Therapy Clinical trials for new therapies may take 10-15 years to complete and cost $1B for one new drug Using molecular data (genetic, metabolomic and microbiome), data mining techniques may discover patterns of response (positive and negative, efficacy and side effects) to therapy associated with molecular profiles allowing: Definitive trial results obtained faster Trial results associated with molecular profiles to guide recommendations for favorable and unfavorable scenarios for therapy